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Approximation Algorithms
Most natural optimization problems, including those arising in important application areas, are NP-hard. Therefore, under the widely believed conjecture that P≠NP, their exact solution is prohibitively time consuming. Charting the landscape of approximability of these problems, via polynomial-time algorithms, therefore becomes a compelling subject of scientific inquiry in computer science and mathematics. This book presents the theory of approximation algorithms.
This book is divided into three parts. Part I covers combinatorial algorithms for a number of important problems, using a wide variety of algorithm design techniques. Part II presents linear programming based algorithms. These are categorized under two fundamental rounding and the primal-dual schema. Part III covers four important the first is the problem of finding a shortest vector in a lattice; the second is the approximability of counting, as opposed to optimization, problems; the third topic is centered around recent breakthrough results, establishing hardness of approximation for many key problems, and giving new legitimacy to approximation algorithms as a deep theory; and the fourth topic consists of the numerous open problems of this young field.
This book is suitable for use in advanced undergraduate and graduate-level courses on approximation algorithms. An undergraduate course in algorithms and the theory of NP-completeness should suffice as a prerequisite for most of the chapters. This book can also be used as supplementary text in basic undergraduate and graduate algorithms courses.
This book is divided into three parts. Part I covers combinatorial algorithms for a number of important problems, using a wide variety of algorithm design techniques. Part II presents linear programming based algorithms. These are categorized under two fundamental rounding and the primal-dual schema. Part III covers four important the first is the problem of finding a shortest vector in a lattice; the second is the approximability of counting, as opposed to optimization, problems; the third topic is centered around recent breakthrough results, establishing hardness of approximation for many key problems, and giving new legitimacy to approximation algorithms as a deep theory; and the fourth topic consists of the numerous open problems of this young field.
This book is suitable for use in advanced undergraduate and graduate-level courses on approximation algorithms. An undergraduate course in algorithms and the theory of NP-completeness should suffice as a prerequisite for most of the chapters. This book can also be used as supplementary text in basic undergraduate and graduate algorithms courses.
399 pages, Hardcover
First published December 5, 2002
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Displaying 1 - 2 of 2 reviews
embarrassed-not-to-have-read
March 20, 2009Amazon 2009-03-19. I was sparked to finally order this by Alan Fay adding it with five stars, and I'm certainly not going to allow myself to be outread by Alan Fay when it comes to theoretical computer science books written at my own Institute of Technology. Vazirani keeps the royalty statements for this book, courtesy of Cambridge Publishing, taped to the outside of his office -- he makes not cent one, in his own self-sacrificing protest against the price of scientific literature. I've said it once, and I'll say it again: Vazirani for President!
April 17, 2008
the best you can do for approximation algorithms. it's pretty readable and complete.
Displaying 1 - 2 of 2 reviews